code: def test_original_features_can_be_automatically_removed_from_result_frame():
      target = "survived"
      teColumns = ["cabin", "embarked"]
      trainingFrame = h2o.import_file(
          pu.locate("smalldata/gbm_test/titanic.csv"), header=1
      )
  
      trainingFrame[target] = trainingFrame[target].asfactor()
  
      te = H2OTargetEncoderEstimator(
          keep_original_categorical_columns=False
      )
      te.train(training_frame=trainingFrame, x=teColumns, y=target)
  
      transformed = te.transform(trainingFrame)
      for col in teColumns:
          assert "{}_te".format(col) in transformed.names
          assert col not in transformed.names
  
file_path: h2o-3/h2o-py/tests/testdir_algos/targetencoder/pyunit_targetencoder_model.py
imports: import sys, os
  import tempfile
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OTargetEncoderEstimator
  from h2o.estimators import H2OGradientBoostingEstimator
label_desc: Use image node_id 4 for calling a global function with example usage: test_original_features_can_be_automatically_removed_from_result_frame() without return types